Spotify and Youtube EDA-Capstone Project¶

In [1]:
import pandas as pd
import numpy as np

import plotly.express as px
import plotly.graph_objects as go
import plotly.figure_factory as ff
from plotly.subplots import make_subplots

import matplotlib.pyplot as plt

import seaborn as sns

from IPython.display import Image
In [2]:
image_path = "C:/Users/Elif/Desktop/spt.png"
Image(image_path)
Out[2]:
In [3]:
df = pd.read_csv(r"C:\Users\Elif\Desktop\Bitirme Projesi\asset-v1 Kodlasam+VAM-03+2023_02+type@asset+block@asset-v1_Kodlasam_MS01_2023_06_type_asset_block_Spotify_Youtube.csv")
In [4]:
df.head()
Out[4]:
Unnamed: 0 Artist Url_spotify Track Album Album_type Uri Danceability Energy Key ... Url_youtube Title Channel Views Likes Comments Description Licensed official_video Stream
0 0 Gorillaz https://open.spotify.com/artist/3AA28KZvwAUcZu... Feel Good Inc. Demon Days album spotify:track:0d28khcov6AiegSCpG5TuT 0.818 0.705 6.0 ... https://www.youtube.com/watch?v=HyHNuVaZJ-k Gorillaz - Feel Good Inc. (Official Video) Gorillaz 693555221.0 6220896.0 169907.0 Official HD Video for Gorillaz' fantastic trac... True True 1.040235e+09
1 1 Gorillaz https://open.spotify.com/artist/3AA28KZvwAUcZu... Rhinestone Eyes Plastic Beach album spotify:track:1foMv2HQwfQ2vntFf9HFeG 0.676 0.703 8.0 ... https://www.youtube.com/watch?v=yYDmaexVHic Gorillaz - Rhinestone Eyes [Storyboard Film] (... Gorillaz 72011645.0 1079128.0 31003.0 The official video for Gorillaz - Rhinestone E... True True 3.100837e+08
2 2 Gorillaz https://open.spotify.com/artist/3AA28KZvwAUcZu... New Gold (feat. Tame Impala and Bootie Brown) New Gold (feat. Tame Impala and Bootie Brown) single spotify:track:64dLd6rVqDLtkXFYrEUHIU 0.695 0.923 1.0 ... https://www.youtube.com/watch?v=qJa-VFwPpYA Gorillaz - New Gold ft. Tame Impala & Bootie B... Gorillaz 8435055.0 282142.0 7399.0 Gorillaz - New Gold ft. Tame Impala & Bootie B... True True 6.306347e+07
3 3 Gorillaz https://open.spotify.com/artist/3AA28KZvwAUcZu... On Melancholy Hill Plastic Beach album spotify:track:0q6LuUqGLUiCPP1cbdwFs3 0.689 0.739 2.0 ... https://www.youtube.com/watch?v=04mfKJWDSzI Gorillaz - On Melancholy Hill (Official Video) Gorillaz 211754952.0 1788577.0 55229.0 Follow Gorillaz online:\nhttp://gorillaz.com \... True True 4.346636e+08
4 4 Gorillaz https://open.spotify.com/artist/3AA28KZvwAUcZu... Clint Eastwood Gorillaz album spotify:track:7yMiX7n9SBvadzox8T5jzT 0.663 0.694 10.0 ... https://www.youtube.com/watch?v=1V_xRb0x9aw Gorillaz - Clint Eastwood (Official Video) Gorillaz 618480958.0 6197318.0 155930.0 The official music video for Gorillaz - Clint ... True True 6.172597e+08

5 rows × 28 columns

In [5]:
df.shape
Out[5]:
(20718, 28)
In [6]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 20718 entries, 0 to 20717
Data columns (total 28 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   Unnamed: 0        20718 non-null  int64  
 1   Artist            20718 non-null  object 
 2   Url_spotify       20718 non-null  object 
 3   Track             20718 non-null  object 
 4   Album             20718 non-null  object 
 5   Album_type        20718 non-null  object 
 6   Uri               20718 non-null  object 
 7   Danceability      20716 non-null  float64
 8   Energy            20716 non-null  float64
 9   Key               20716 non-null  float64
 10  Loudness          20716 non-null  float64
 11  Speechiness       20716 non-null  float64
 12  Acousticness      20716 non-null  float64
 13  Instrumentalness  20716 non-null  float64
 14  Liveness          20716 non-null  float64
 15  Valence           20716 non-null  float64
 16  Tempo             20716 non-null  float64
 17  Duration_ms       20716 non-null  float64
 18  Url_youtube       20248 non-null  object 
 19  Title             20248 non-null  object 
 20  Channel           20248 non-null  object 
 21  Views             20248 non-null  float64
 22  Likes             20177 non-null  float64
 23  Comments          20149 non-null  float64
 24  Description       19842 non-null  object 
 25  Licensed          20248 non-null  object 
 26  official_video    20248 non-null  object 
 27  Stream            20142 non-null  float64
dtypes: float64(15), int64(1), object(12)
memory usage: 4.4+ MB
In [7]:
sütun_adları = df.columns.tolist()
print("Sütun Adları:", sütun_adları)

Sütun Adları: ['Unnamed: 0', 'Artist', 'Url_spotify', 'Track', 'Album', 'Album_type', 'Uri', 'Danceability', 'Energy', 'Key', 'Loudness', 'Speechiness', 'Acousticness', 'Instrumentalness', 'Liveness', 'Valence', 'Tempo', 'Duration_ms', 'Url_youtube', 'Title', 'Channel', 'Views', 'Likes', 'Comments', 'Description', 'Licensed', 'official_video', 'Stream']
In [8]:
# Sadece sayısal sütunları seçerek açıklayıcı istatistikler hesaplayalım.
numerical_columns = df.select_dtypes(include=['float64'])
numerical_description = numerical_columns.describe()
print(numerical_description)

       Danceability        Energy           Key      Loudness   Speechiness  \
count  20716.000000  20716.000000  20716.000000  20716.000000  20716.000000   
mean       0.619777      0.635250      5.300348     -7.671680      0.096456   
std        0.165272      0.214147      3.576449      4.632749      0.111960   
min        0.000000      0.000020      0.000000    -46.251000      0.000000   
25%        0.518000      0.507000      2.000000     -8.858000      0.035700   
50%        0.637000      0.666000      5.000000     -6.536000      0.050500   
75%        0.740250      0.798000      8.000000     -4.931000      0.103000   
max        0.975000      1.000000     11.000000      0.920000      0.964000   

       Acousticness  Instrumentalness      Liveness       Valence  \
count  20716.000000      20716.000000  20716.000000  20716.000000   
mean       0.291535          0.055962      0.193521      0.529853   
std        0.286299          0.193262      0.168531      0.245441   
min        0.000001          0.000000      0.014500      0.000000   
25%        0.045200          0.000000      0.094100      0.339000   
50%        0.193000          0.000002      0.125000      0.537000   
75%        0.477250          0.000463      0.237000      0.726250   
max        0.996000          1.000000      1.000000      0.993000   

              Tempo   Duration_ms         Views         Likes      Comments  \
count  20716.000000  2.071600e+04  2.024800e+04  2.017700e+04  2.014900e+04   
mean     120.638340  2.247176e+05  9.393782e+07  6.633411e+05  2.751899e+04   
std       29.579018  1.247905e+05  2.746443e+08  1.789324e+06  1.932347e+05   
min        0.000000  3.098500e+04  0.000000e+00  0.000000e+00  0.000000e+00   
25%       97.002000  1.800095e+05  1.826002e+06  2.158100e+04  5.090000e+02   
50%      119.965000  2.132845e+05  1.450110e+07  1.244810e+05  3.277000e+03   
75%      139.935000  2.524430e+05  7.039975e+07  5.221480e+05  1.436000e+04   
max      243.372000  4.676058e+06  8.079649e+09  5.078865e+07  1.608314e+07   

             Stream  
count  2.014200e+04  
mean   1.359422e+08  
std    2.441321e+08  
min    6.574000e+03  
25%    1.767486e+07  
50%    4.968298e+07  
75%    1.383581e+08  
max    3.386520e+09
In [9]:
df.duplicated()
Out[9]:
0        False
1        False
2        False
3        False
4        False
         ...  
20713    False
20714    False
20715    False
20716    False
20717    False
Length: 20718, dtype: bool
In [10]:
df.isnull().sum()
Out[10]:
Unnamed: 0            0
Artist                0
Url_spotify           0
Track                 0
Album                 0
Album_type            0
Uri                   0
Danceability          2
Energy                2
Key                   2
Loudness              2
Speechiness           2
Acousticness          2
Instrumentalness      2
Liveness              2
Valence               2
Tempo                 2
Duration_ms           2
Url_youtube         470
Title               470
Channel             470
Views               470
Likes               541
Comments            569
Description         876
Licensed            470
official_video      470
Stream              576
dtype: int64
In [11]:
df.nunique()
Out[11]:
Unnamed: 0          20718
Artist               2079
Url_spotify          2079
Track               17841
Album               11937
Album_type              3
Uri                 18862
Danceability          898
Energy               1268
Key                    12
Loudness             9417
Speechiness          1303
Acousticness         3138
Instrumentalness     4012
Liveness             1536
Valence              1293
Tempo               15024
Duration_ms         14690
Url_youtube         18154
Title               18146
Channel              6714
Views               19245
Likes               17939
Comments            10485
Description         17395
Licensed                2
official_video          2
Stream              18461
dtype: int64

Veri temizleme ve Dönüştürme¶

In [12]:
##Kullanmayacağımız gereksiz sütunları silelim
df.drop(['Unnamed: 0','Url_spotify','Uri', 'Url_youtube', 'Title', 'Description'],axis =1, inplace = True)
In [13]:
##Tablodaki Eksik Değerleri Kaldıralım
df.dropna(inplace = True)
In [14]:
df.isnull().sum()
Out[14]:
Artist              0
Track               0
Album               0
Album_type          0
Danceability        0
Energy              0
Key                 0
Loudness            0
Speechiness         0
Acousticness        0
Instrumentalness    0
Liveness            0
Valence             0
Tempo               0
Duration_ms         0
Channel             0
Views               0
Likes               0
Comments            0
Licensed            0
official_video      0
Stream              0
dtype: int64

Keşifsel Veri Analizi(EDAs)¶

In [15]:
print(df.columns)

Index(['Artist', 'Track', 'Album', 'Album_type', 'Danceability', 'Energy',
       'Key', 'Loudness', 'Speechiness', 'Acousticness', 'Instrumentalness',
       'Liveness', 'Valence', 'Tempo', 'Duration_ms', 'Channel', 'Views',
       'Likes', 'Comments', 'Licensed', 'official_video', 'Stream'],
      dtype='object')
In [5]:
import plotly.graph_objects as go
import pandas as pd

# Her bir sanatçının Energy, Danceability ve Valence özelliklerinin ortalamasını hesaplayalım
artist_metrics = df.groupby('Artist')[['Energy', 'Danceability', 'Valence']].mean().reset_index()

# En üst 20 sanatçıyı seçelim
top_20_artists = artist_metrics.sort_values(by='Energy', ascending=False).head(20)

# Canlı renkler listesi
colors = ['lightgreen', 'limegreen', 'darkgreen']

# Plotly ile her bir metrik için ayrı grafik oluşturalım

# Energy metriği için grafik
fig1 = go.Figure()
fig1.add_trace(go.Bar(x=top_20_artists['Artist'], y=top_20_artists['Energy'], name='Enerji Seviyesi', marker_color=colors[0],
text=top_20_artists['Energy'], textposition='outside', texttemplate='%{text:.2f}'))
fig1.update_layout(title='En İyi 20 Sanatçıya Göre Ortalama Enerji Seviyesi', xaxis_title='Sanatçı', 
yaxis_title='Enerji Seviyesi')

# Danceability metriği için grafik
fig2 = go.Figure()
fig2.add_trace(go.Bar(x=top_20_artists['Artist'], y=top_20_artists['Danceability'], name='Dans Edilebilirlik',
marker_color=colors[1], text=top_20_artists['Danceability'], textposition='outside', texttemplate='%{text:.2f}'))
fig2.update_layout(title='En İyi 20 Sanatçıya Göre Ortalama Dans Edilebilirlik', xaxis_title='Sanatçı',
yaxis_title='Dans Edilebilirlik')

# Valence metriği için grafik
fig3 = go.Figure()
fig3.add_trace(go.Bar(x=top_20_artists['Artist'], y=top_20_artists['Valence'], name='Müzikal Pozitiflik',
marker_color=colors[2], text=top_20_artists['Valence'], textposition='outside', texttemplate='%{text:.2f}'))
fig3.update_layout(title='En İyi 20 Sanatçıya Göre Ortalama Müzikal Pozitiflik', xaxis_title='Sanatçı',
yaxis_title='Müzikal Pozitiflik')

# Grafikleri düzenleme
fig1.show()
fig2.show()
fig3.show()
In [17]:
import plotly.graph_objects as go

# Albüm türlerini say ve veri kümesini oluştur
album_types = df['Album_type'].value_counts().reset_index()
album_types.columns = ['Album Type', 'Count']

# Verileri hazırla
labels = album_types['Album Type'].tolist()
sizes = album_types['Count'].tolist()

# Pasta grafiğini oluştur
fig = go.Figure(data=[go.Pie(labels=labels, values=sizes, 
                             textinfo='label+percent', 
                             insidetextorientation='radial',
                             hole=0.3,
                             marker=dict(colors=['orange', 'darkgreen', 'lightgreen']))])

# Grafiği düzenle
fig.update_layout(title='Albüm Türlerine Göre Dağılım')

# Grafiği göster
fig.show()
In [18]:
import plotly.graph_objects as go

# Scatter plot oluştur
fig = go.Figure(data=go.Scatter(x=df['Views'], y=df['Likes'], 
                                 mode='markers', marker=dict(color='green', opacity=0.7, line=dict(color='black', width=1))))

# Trendline ekle
import numpy as np
z = np.polyfit(df['Views'], df['Likes'], 1)
p = np.poly1d(z)
fig.add_trace(go.Scatter(x=df['Views'], y=p(df['Views']), mode='lines', line=dict(color='orange', dash='dash')))

# Grafiği düzenle
fig.update_layout(title='Görüntüler ile Beğeniler Arasındaki İlişki',
                  xaxis_title='Görüntüler',
                  yaxis_title='Beğeniler',
                  showlegend=False,
                  plot_bgcolor='white')

# Grafiği göster
fig.show()
In [ ]:
import plotly.graph_objects as go

# Scatter plot oluştur
fig = go.Figure(data=go.Scatter(x=df['Views'], y=df['Comments'], 
                                 mode='markers', marker=dict(color='darkorange', opacity=0.7, line=dict(color='black', width=1))))

# Trendline ekle
import numpy as np
z = np.polyfit(df['Views'], df['Comments'], 1)
p = np.poly1d(z)
fig.add_trace(go.Scatter(x=df['Views'], y=p(df['Views']), mode='lines', line=dict(color='green', dash='dash')))

# Grafiği düzenle
fig.update_layout(title='Görüntüler ile Yorumlar Arasındaki İlişki',
                  xaxis_title='Görüntüler',
                  yaxis_title='Yorumlar',
                  showlegend=False,
                  plot_bgcolor='white')

# Grafiği göster
fig.show()
In [26]:
import plotly.graph_objects as go
import pandas as pd

# Sayısal sütunları seç ve korelasyon matrisini hesaplayalım
numeric_df = df.select_dtypes(include='number')
correlation_matrix = numeric_df.corr().round(2)

# Isı haritası oluşturma
fig = go.Figure(data=go.Heatmap(z=correlation_matrix.values,
                                 x=correlation_matrix.columns,
                                 y=correlation_matrix.index,
                                 colorscale='Oranges',  
                                 colorbar=dict(title='Korelasyon')))

# Kareler üzerine sayısal değerleri ekleyelim
for i in range(len(correlation_matrix.index)):
    for j in range(len(correlation_matrix.columns)):
        value = correlation_matrix.values[i, j]
        if value > 0.95:
            color = 'white'
        else:
            color = 'black'
        fig.add_annotation(x=correlation_matrix.columns[j], y=correlation_matrix.index[i],
                           text=str(value),
                           showarrow=False, font=dict(color=color, size=10),
                           xanchor='center', yanchor='middle')

# Grafiği düzenleme
fig.update_layout(title='Kolonlar Arasındaki Korelasyon Matrisi')

# Grafiği göster
fig.show()
In [ ]: